Dgraph Database Example
The Dgraph Documentation says:
Dgraph is a liberally licensed, scalable, distributed, highly available and fast graph database, designed from ground up to be run in production.
In this example I want to see how to work with Dgraph in .NET.
Starting Dgraph
Initialize the Dgraph Cluster:
dgraph.exe zero
Start the Dgraph Server (with a 2048 MB LRU Cache):
dgraph server -l 2048
Example Query
Dgraph GraqhQL+- Query to query departures from Los Angeles Airport (LAX):
{
lax_departures(func: eq(origin, "LAX")) {
flight_no,
year,
month,
day_of_month,
origin,
destination,
arrival_delay,
departure_delay,
weather_delay
}
}
I put the query in a file los_angeles_departures.txt and query Dgraph with curl:
curl localhost:8080/query -X POST -d @los_angeles_departures.txt
And it yields the following result:
{
"data": {
"lax_departures": [
{
"flight_no": "457",
"year": 2014,
"month": 1,
"day_of_month": 26,
"origin": "LAX",
"destination": "SEA",
"arrival_delay": 6,
"departure_delay": -6,
"weather_delay": 0
},
{
"flight_no": "467",
"year": 2014,
"month": 1,
"day_of_month": 25,
"origin": "LAX",
"destination": "SEA",
"arrival_delay": -20,
"departure_delay": -8,
"weather_delay": 0
},
{
"flight_no": "453",
"year": 2014,
"month": 1,
"day_of_month": 25,
"origin": "LAX",
"destination": "SEA",
"arrival_delay": -23,
"departure_delay": -10,
"weather_delay": 0
},
{
"flight_no": "561",
"year": 2014,
"month": 1,
"day_of_month": 26,
"origin": "LAX",
"destination": "PDX",
"arrival_delay": -12,
"departure_delay": -12,
"weather_delay": 0
},
{
"flight_no": "469",
"year": 2014,
"month": 1,
"day_of_month": 26,
"origin": "LAX",
"destination": "SEA",
"arrival_delay": 15,
"departure_delay": -5,
"weather_delay": 0
},
{
"flight_no": "445",
"year": 2014,
"month": 1,
"day_of_month": 25,
"origin": "LAX",
"destination": "SEA",
"arrival_delay": -15,
"departure_delay": -2,
"weather_delay": 0
},
{
"flight_no": "461",
"year": 2014,
"month": 1,
"day_of_month": 25,
"origin": "LAX",
"destination": "SEA",
"arrival_delay": -16,
"departure_delay": -4,
"weather_delay": 0
},
{
"flight_no": "6",
"year": 2014,
"month": 1,
"day_of_month": 26,
"origin": "LAX",
"destination": "DCA",
"arrival_delay": -25,
"departure_delay": -5,
"weather_delay": 0
},
{
"flight_no": "453",
"year": 2014,
"month": 1,
"day_of_month": 26,
"origin": "LAX",
"destination": "SEA",
"arrival_delay": 8,
"departure_delay": 3,
"weather_delay": 0
},
{
"flight_no": "477",
"year": 2014,
"month": 1,
"day_of_month": 26,
"origin": "LAX",
"destination": "SEA",
"arrival_delay": -25,
"departure_delay": -10,
"weather_delay": 0
},
{
"flight_no": "461",
"year": 2014,
"month": 1,
"day_of_month": 26,
"origin": "LAX",
"destination": "SEA",
"arrival_delay": -2,
"departure_delay": -7,
"weather_delay": 0
}
]
},
"extensions": {
"server_latency": {
"processing_ns": 76998700,
"encoding_ns": 45999000
},
"txn": { "start_ts": 402 }
}
}TODO
There are many things I need to look into like Node UIDs, Batch Processing, Indexes, Facets, Edges, RDF. So far this repository is a working example for reading the Airline on Time Performance Data from CSV into Dgraph, and make a first simple query to get data out of it. More to come as soon as I find the time ...
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